Spatial skyline query problem in Euclidean and road-network spaces

With the growth of data-intensive applications, along with the increase of both size and dimensionality of data, queries with advanced semantics have recently drawn researchers' attention. Skyline query problem is one of them, which produces optimal results based on user preferences. In this thesis, we study the problem of spatial skyline query in the Euclidean and road network spaces. For a given data set P, we are required to compute the spatial skyline points of P with respect to an arbitrary query set Q. A point p ∈ P is a spatial skyline point if and only if, for any other data point r ∈ P , p is closer to at least one query point q ∈ Q as compared to r and has in the best case the same distance as r to the rest of the query points. We propose several efficient algorithms that outperform the existing algorithms.